1. Path Planning Optimization for UAVs over Inhabited Areas
Anoosh Reddy
College Park Scholars – Science & Global Change Program
Computer Engineering
College Park Scholars Academic Showcase, May 4, 2017
Introduction:
In my ongoing project, we are working on optimizing and scaling up an algorithm to determine the optimal path for a UAV to fly over inhabited areas, while balancing time taken and risk for the people below.
Abstract:
When flying UAVs over inhabited areas, a major concern is safety. Namely, what will happen to the people below if the UAV crashes? The problem this research looks to solve, is the problem of optimization. The safest path will likely be indirect and long, while the fastest path might be too risky. The algorithm to balance these factors was written in MATLAB, which uses a network optimization approach on the problem. This involves creating a graph for the problem, with nodes on turning points. Creating a graph involves processing population density data, which can take a long time due to the amount of raw data.
A network path generated from Camp David, MD to Naval Systems Air Command base in Patuxent River, MD.
The same path generated from Camp David to Naval Systems Air Base, showing the total grid of flight paths.
Discussion:
My main goal was finding ways to scale the algorithm to the rest of the country. The main bottleneck involves generating networks for new areas, since the population datasets are very large. The current data is census bureau data from 2010, and I am also looking into alternate data sources, like the Landscan Global Population dataset. I worked on a way to store the networks, which are stored as MATLAB matrices, and am currently working on a way to load pre-generated networks, so it is unnecessary to do the lengthy computations for the network generated each time.
Population of the custom area of approximately College Park generated by Landscan.
Site Information:
Name of Site: Department of Mechanical Engineering, Martin Hall
Address: 2181 Glenn L. Martin Hall, Building 088, University of Maryland, College Park, 20742
Site mission: The mission of this project is to enhance the UAV risk-based path optimization software, primarily through MATLAB code, and adapt the algorithm to work for the entire United States.
Future Work:
The end goal for this process is to be able to apply this algorithm to the entire United States, and be able to streamline the process to a reasonable runtime. Once the process of generating grids for new areas is optimized, the next step is to load the information into an application for consumers to be able to use independently. A prototype of the application is shown here, with the DC/MD/VA area preloaded in.
Acknowledgments:
I would like to thank my supervisors, Dr.Azarm Shapour and Dr.Jeffrey Hermann, for mentoring me on my project. I would also like to thank Dr.Holtz and Dr.Merck for running their guidance throughout this project, and the last two years in the Science and Global change program.
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